OpenRaiser/Intern-Atlas huggingface dataset:
Can You Traducir This? Machine Translation for Code-Switched Input
Venue: arXiv cs.CL 2021
Year: 2021

Code-Switching (CSW) is a common phenomenon that occurs in multilingual geographic or social contexts, which raises challenging problems for natural language processing tools. We focus here on Machine Translation (MT) of CSW texts, where we aim to simultaneously disentangle and translate the two mixed languages. Due to the lack of actual translated CSW data, we generate artificial training data from regular parallel texts. Experiments show this training strategy yields MT systems that surpass multilingual systems for code-switched texts. These results are confirmed in an alternative task aimed at providing contextual translations for a L2 writing assistant.
Forecast Analysis of the COVID-19 Incidence in Lebanon: Prediction of Future Epidemiological Trends to Plan More Effective Control Programs
Venue: arXiv cs.AI 2021
Year: 2021

Ever since the COVID-19 pandemic started, all the governments have been trying to limit its effects on their citizens and countries. This pandemic was harsh on different levels for almost all populations worldwide and this is what drove researchers and scientists to get involved and work on several kinds of simulations to get a better insight into this virus and be able to stop it the earliest possible. In this study, we simulate the spread of COVID-19 in Lebanon using an Agent-Based Model where people are modeled as agents that have specific characteristics and behaviors determined from statistical distributions using Monte Carlo Algorithm. These agents can go into the world, interact with each other, and thus, infect each other. This is how the virus spreads. During the simulation, we can introduce different Non-Pharmaceutical Interventions - or more commonly NPIs - that aim to limit the spread of the virus (wearing a mask, closing locations, etc). Our Simulator was first validated on concepts (e.g. Flattening the Curve and Second Wave scenario), and then it was applied on the case of Lebanon. We studied the effect of opening schools and universities on the pandemic situation in the country since the Lebanese Ministry of Education is planning to do so progressively, starting from 21 April 2021. Based on the results we obtained, we conclude that it would be better to delay the school openings while the vaccination campaign is still slow in the country.
Image Classification with Classic and Deep Learning Techniques
Venue: arXiv cs.CV 2021
Year: 2021

To classify images based on their content is one of the most studied topics in the field of computer vision. Nowadays, this problem can be addressed using modern techniques such as Convolutional Neural Networks (CNN), but over the years different classical methods have been developed. In this report, we implement an image classifier using both classic computer vision and deep learning techniques. Specifically, we study the performance of a Bag of Visual Words classifier using Support Vector Machines, a Multilayer Perceptron, an existing architecture named InceptionV3 and our own CNN, TinyNet, designed from scratch. We evaluate each of the cases in terms of accuracy and loss, and we obtain results that vary between 0.6 and 0.96 depending on the model and configuration used.
Asynchronous Template Games and the Gray Tensor Product of 2-Categories
Venue: arXiv cs.LO 2021
Year: 2021

In his recent and exploratory work on template games and linear logic, Melliès defines sequential and concurrent games as categories with positions as objects and trajectories as morphisms, labelled by a specific synchronization template. In the present paper, we bring the idea one dimension higher and advocate that template games should not be just defined as 1-dimensional categories but as 2-dimensional categories of positions, trajectories and reshufflings (or reschedulings) as 2-cells. In order to achieve the purpose, we take seriously the parallel between asynchrony in concurrency and the Gray tensor product of 2-categories. One technical difficulty on the way is that the category S=2-Cat of small 2-categories equipped with the Gray tensor product is monoidal, and not cartesian. This prompts us to extend the framework of template games originally formulated by Melliès in a category S with finite limits, and to upgrade it in the style of Aguiar's work on quantum groups to the more general situation of a monoidal category S with coreflexive equalizers, preserved by the tensor product componentwise. We construct in this way an asynchronous template game semantics of multiplicative additive linear logic (MALL) where every formula and every proof is interpreted as a labelled 2-category equipped, respectively, with the structure of Gray comonoid for asynchronous template games, and of Gray bicomodule for asynchronous strategies.
Sparse image reconstruction on the sphere: a general approach with uncertainty quantification
Venue: arXiv cs.IT 2021
Year: 2021

Inverse problems defined naturally on the sphere are becoming increasingly of interest. In this article we provide a general framework for evaluation of inverse problems on the sphere, with a strong emphasis on flexibility and scalability. We consider flexibility with respect to the prior selection (regularization), the problem definition - specifically the problem formulation (constrained/unconstrained) and problem setting (analysis/synthesis) - and optimization adopted to solve the problem. We discuss and quantify the trade-offs between problem formulation and setting. Crucially, we consider the Bayesian interpretation of the unconstrained problem which, combined with recent developments in probability density theory, permits rapid, statistically principled uncertainty quantification (UQ) in the spherical setting. Linearity is exploited to significantly increase the computational efficiency of such UQ techniques, which in some cases are shown to permit analytic solutions. We showcase this reconstruction framework and UQ techniques on a variety of spherical inverse problems. The code discussed throughout is provided under a GNU general public license, in both C++ and Python.
Predicting Gene-Disease Associations with Knowledge Graph Embeddings over Multiple Ontologies
Venue: arXiv cs.LG 2021
Year: 2021

Ontology-based approaches for predicting gene-disease associations include the more classical semantic similarity methods and more recently knowledge graph embeddings. While semantic similarity is typically restricted to hierarchical relations within the ontology, knowledge graph embeddings consider their full breadth. However, embeddings are produced over a single graph and complex tasks such as gene-disease association may require additional ontologies. We investigate the impact of employing richer semantic representations that are based on more than one ontology, able to represent both genes and diseases and consider multiple kinds of relations within the ontologies. Our experiments demonstrate the value of employing knowledge graph embeddings based on random-walks and highlight the need for a closer integration of different ontologies.
Dealing with Variability in API Misuse Specification
Venue: arXiv cs.CR 2021
Year: 2021

APIs are the primary mechanism for developers to gain access to externally defined services and tools. However, previous research has revealed API misuses that violate the contract of APIs to be prevalent. Such misuses can have harmful consequences, especially in the context of cryptographic libraries. Various API misuse detectors have been proposed to address this issue including CogniCrypt, one of the most versatile of such detectors and that uses a language CrySL to specify cryptographic API usage contracts. Nonetheless, existing approaches to detect API misuse had not been designed for systematic reuse, ignoring the fact that different versions of a library, different versions of a platform, and different recommendations or guidelines might introduce variability in the correct usage of an API. Yet, little is known about how such variability impacts the specification of the correct API usage. This paper investigates this question by analyzing the impact of various sources of variability on widely used Java cryptographic libraries including JCA, Bouncy Castle, and Google Tink. The results of our investigation show that sources of variability like new versions of the API and security standards significantly impact the specifications. We then use the insights gained from our investigation to motivate an extension to the CrySL language named MetaCrySL, which builds on meta programming concepts. We evaluate MetaCrySL by specifying usage rules for a family of Android versions and illustrate that MetaCrySL can model all forms of variability we identified and drastically reduce the size of a family of specifications for the correct usage of cryptographic APIs
Towards the Use of Slice-based Cohesion Metrics with Learning Analytics to Assess Programming Skills
Venue: arXiv cs.SE 2021
Year: 2021

In programming education, it makes a difference whether you are dealing with beginners or advanced students. As our future students will become even more tech-savvy, it is necessary to assess programming skills appropriately and quickly to protect them from boredom and optimally support the learning process. In this work, we advocate for the use of slice-based cohesion metrics to assess the process of program construction in a learning analytics setting. We argue that semantically related parts during program construction are an essential part of programming skills. Therefore, we propose using cohesion metrics on the level of variables to identify programmers' trains of thought based on the cohesion of semantically related parts during program construction.
Graph Theory for Metro Traffic Modelling
Venue: arXiv cs.LG 2021
Year: 2021

A unifying graph theoretic framework for the modelling of metro transportation networks is proposed. This is achieved by first introducing a basic graph framework for the modelling of the London underground system from a diffusion law point of view. This forms a basis for the analysis of both station importance and their vulnerability, whereby the concept of graph vertex centrality plays a key role. We next explore k-edge augmentation of a graph topology, and illustrate its usefulness both for improving the network robustness and as a planning tool. Upon establishing the graph theoretic attributes of the underlying graph topology, we proceed to introduce models for processing data on such a metro graph. Commuter movement is shown to obey the Fick's law of diffusion, where the graph Laplacian provides an analytical model for the diffusion process of commuter population dynamics. Finally, we also explore the application of modern deep learning models, such as graph neural networks and hyper-graph neural networks, as general purpose models for the modelling and forecasting of underground data, especially in the context of the morning and evening rush hours. Comprehensive simulations including the passenger in- and out-flows during the morning rush hour in London demonstrates the advantages of the graph models in metro planning and traffic management, a formal mathematical approach with wide economic implications.
Engineering and Experimentally Benchmarking a Serverless Edge Computing System
Venue: arXiv cs.NI 2021
Year: 2021

Thanks to the latest advances in containerization, the serverless edge computing model is becoming close to reality. Serverless at the edge is expected to enable low latency applications with fast autoscaling mechanisms, all running on heterogeneous and resource-constrained devices. In this work, we engineer and experimentally benchmark a serverless edge computing system architecture. We deploy a decentralized edge computing platform for serverless applications providing processing, storage, and communication capabilities using only open-source software, running over heterogeneous resources (e.g., virtual machines, Raspberry Pis, or bare metal servers, etc). To achieve that, we provision an overlay-network based on Nebula network agnostic technology, running over private or public networks, and use K3s to provide hardware abstraction. We benchmark the system in terms of response times, throughput and scalability using different hardware devices connected through the public Internet. The results show that while serverless is feasible on heterogeneous devices showing a good performance on constrained devices, such as Raspberry Pis, the lack of support when determining computational power and network characterization leaves much room for improvement in edge environments.
Instance-aware Remote Sensing Image Captioning with Cross-hierarchy Attention
Venue: arXiv cs.CV 2021
Year: 2021

The spatial attention is a straightforward approach to enhance the performance for remote sensing image captioning. However, conventional spatial attention approaches consider only the attention distribution on one fixed coarse grid, resulting in the semantics of tiny objects can be easily ignored or disturbed during the visual feature extraction. Worse still, the fixed semantic level of conventional spatial attention limits the image understanding in different levels and perspectives, which is critical for tackling the huge diversity in remote sensing images. To address these issues, we propose a remote sensing image caption generator with instance-awareness and cross-hierarchy attention. 1) The instances awareness is achieved by introducing a multi-level feature architecture that contains the visual information of multi-level instance-possible regions and their surroundings. 2) Moreover, based on this multi-level feature extraction, a cross-hierarchy attention mechanism is proposed to prompt the decoder to dynamically focus on different semantic hierarchies and instances at each time step. The experimental results on public datasets demonstrate the superiority of proposed approach over existing methods.
Wi-Lo: Emulating LoRa using COTS WiFi
Venue: arXiv cs.NI 2021
Year: 2021

We present Wi-Lo, which allows to convert an ordinary 802.11 (WiFi) access point into an internet of things (IoT) gateway supporting the low-power wide area network (LPWAN) technology LoRa in the downlink. Our Wi-Lo system only requires a software update and no additional hardware. It uses signal emulation technique based on complementary code keying modulation from 802.11b in order to emulate a downlink LoRa (long range) transmission. The Wi-Lo gateway can be used by a normal WiFi-enabled smartphone to send packets to LoRa compliant IoT devices like smart sensors. We implemented a prototype using commodity WiFi hardware. Experimental results show that Wi-Lo enables a normal WiFi node to communication to LoRa devices even over long distances, which is comparable to the configurations using pure LoRa transmitter and receivers.
Towards transparency in NLP shared tasks
Venue: arXiv cs.CL 2021
Year: 2021

This article reports on a survey carried out across the Natural Language Processing (NLP) community. The survey aimed to capture the opinions of the research community on issues surrounding shared tasks, with respect to both participation and organisation. Amongst the 175 responses received, both positive and negative observations were made. We carried out and report on an extensive analysis of these responses, which leads us to propose a Shared Task Organisation Checklist that could support future participants and organisers. The proposed Checklist is flexible enough to accommodate the wide diversity of shared tasks in our field and its goal is not to be prescriptive, but rather to serve as a tool that encourages shared task organisers to foreground ethical behaviour, beginning with the common issues that the 175 respondents deemed important. Its usage would not only serve as an instrument to reflect on important aspects of shared tasks, but would also promote increased transparency around them.
Adversarial examples attack based on random warm restart mechanism and improved Nesterov momentum
Venue: arXiv cs.LG 2021
Year: 2021

The deep learning algorithm has achieved great success in the field of computer vision, but some studies have pointed out that the deep learning model is vulnerable to attacks adversarial examples and makes false decisions. This challenges the further development of deep learning, and urges researchers to pay more attention to the relationship between adversarial examples attacks and deep learning security. This work focuses on adversarial examples, optimizes the generation of adversarial examples from the view of adversarial robustness, takes the perturbations added in adversarial examples as the optimization parameter. We propose RWR-NM-PGD attack algorithm based on random warm restart mechanism and improved Nesterov momentum from the view of gradient optimization. The algorithm introduces improved Nesterov momentum, using its characteristics of accelerating convergence and improving gradient update direction in optimization algorithm to accelerate the generation of adversarial examples. In addition, the random warm restart mechanism is used for optimization, and the projected gradient descent algorithm is used to limit the range of the generated perturbations in each warm restart, which can obtain better attack effect. Experiments on two public datasets show that the algorithm proposed in this work can improve the success rate of attacking deep learning models without extra time cost. Compared with the benchmark attack method, the algorithm proposed in this work can achieve better attack success rate for both normal training model and defense model. Our method has average attack success rate of 46.3077%, which is 27.19% higher than I-FGSM and 9.27% higher than PGD. The attack results in 13 defense models show that the attack algorithm proposed in this work is superior to the benchmark algorithm in attack universality and transferability.
Zero-Shot Generalization using Intrinsically Motivated Compositional Emergent Protocols
Venue: arXiv cs.AI 2021
Year: 2021

Human language has been described as a system that makes \textit{use of finite means to express an unlimited array of thoughts}. Of particular interest is the aspect of compositionality, whereby, the meaning of a compound language expression can be deduced from the meaning of its constituent parts. If artificial agents can develop compositional communication protocols akin to human language, they can be made to seamlessly generalize to unseen combinations. Studies have recognized the role of curiosity in enabling linguistic development in children. In this paper, we seek to use this intrinsic feedback in inducing a systematic and unambiguous protolanguage. We demonstrate how compositionality can enable agents to not only interact with unseen objects but also transfer skills from one task to another in a zero-shot setting: \textit{Can an agent, trained to `pull' and `push twice', `pull twice'?}.
Integrating extracted information from bert and multiple embedding methods with the deep neural network for humour detection
Venue: arXiv cs.CL 2021
Year: 2021

Humour detection from sentences has been an interesting and challenging task in the last few years. In attempts to highlight humour detection, most research was conducted using traditional approaches of embedding, e.g., Word2Vec or Glove. Recently BERT sentence embedding has also been used for this task. In this paper, we propose a framework for humour detection in short texts taken from news headlines. Our proposed framework (IBEN) attempts to extract information from written text via the use of different layers of BERT. After several trials, weights were assigned to different layers of the BERT model. The extracted information was then sent to a Bi-GRU neural network as an embedding matrix. We utilized the properties of some external embedding models. A multi-kernel convolution in our neural network was also employed to extract higher-level sentence representations. This framework performed very well on the task of humour detection.
Towards a Model for LSH
Venue: arXiv cs.DB 2021
Year: 2021

As data volumes continue to grow, clustering and outlier detection algorithms are becoming increasingly time-consuming. Classical index structures for neighbor search are no longer sustainable due to the "curse of dimensionality". Instead, approximated index structures offer a good opportunity to significantly accelerate the neighbor search for clustering and outlier detection and to have the lowest possible error rate in the results of the algorithms. Locality-sensitive hashing is one of those. We indicate directions to model the properties of LSH.
Proving LTL Properties of Bitvector Programs and Decompiled Binaries (Extended)
Venue: arXiv cs.PL 2021
Year: 2021

There is increasing interest in applying verification tools to programs that have bitvector operations (eg., binaries). SMT solvers, which serve as a foundation for these tools, have thus increased support for bitvector reasoning through bit-blasting and linear arithmetic approximations. In this paper we show that similar linear arithmetic approximation of bitvector operations can be done at the source level through transformations. Specifically, we introduce new paths that over-approximate bitvector operations with linear conditions/constraints, increasing branching but allowing us to better exploit the well-developed integer reasoning and interpolation of verification tools. We show that, for reachability of bitvector programs, increased branching incurs negligible overhead yet, when combined with integer interpolation optimizations, enables more programs to be verified. We further show this exploitation of integer interpolation in the common case also enables competitive termination verification of bitvector programs and leads to the first effective technique for LTL verification of bitvector programs. Finally, we provide an in-depth case study of decompiled ("lifted") binary programs, which emulate X86 execution through frequent use of bitvector operations. We present a new tool DarkSea, the first tool capable of verifying reachability, termination, and LTL of lifted binaries.
Blocking without Breaking: Identification and Mitigation of Non-Essential IoT Traffic
Venue: arXiv cs.NI 2021
Year: 2021

Despite the prevalence of Internet of Things (IoT) devices, there is little information about the purpose and risks of the Internet traffic these devices generate, and consumers have limited options for controlling those risks. A key open question is whether one can mitigate these risks by automatically blocking some of the Internet connections from IoT devices, without rendering the devices inoperable. In this paper, we address this question by developing a rigorous methodology that relies on automated IoT-device experimentation to reveal which network connections (and the information they expose) are essential, and which are not. We further develop strategies to automatically classify network traffic destinations as either required (i.e., their traffic is essential for devices to work properly) or not, hence allowing firewall rules to block traffic sent to non-required destinations without breaking the functionality of the device. We find that indeed 16 among the 31 devices we tested have at least one blockable non-required destination, with the maximum number of blockable destinations for a device being 11. We further analyze the destination of network traffic and find that all third parties observed in our experiments are blockable, while first and support parties are neither uniformly required or non-required. Finally, we demonstrate the limitations of existing blocklists on IoT traffic, propose a set of guidelines for automatically limiting non-essential IoT traffic, and we develop a prototype system that implements these guidelines.